The gene trees were constructed using ETE toolkit:
Preparatory step: installing Miniconda, following the instructions from http://etetoolkit.org/download/
The gene trees were estimated using RAxML:
ete3 build -w standard_raxml -n /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/FASTA/uce-3.fasta -o /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/FASTA/uce-3-tree
Automatically generate the corresponding command for each of the 230 alignment files in the directory:
cd /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/FASTA
ls
Copy the output to a text file:
# Get the 1st part of the command
locus_table <- read.table("Set1-individual-loci.txt")
n <- length(unlist(locus_table))
inputfile <- vector()
for(i in locus_table) {
inputfile <- append(inputfile, paste("ete3 build -w standard_raxml -n /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/FASTA/", i, sep = ""))
}
# Length check
length(inputfile) - n
# Remove file endings from the locus names
names_and_endings <- as.character(as.vector(as.matrix(locus_table)))
locus_names <- vector()
for(i in names_and_endings) {
locus_names <- append(locus_names, sapply(strsplit(i, split='.', fixed=TRUE), function(x) (x[1])))
}
# Get the 2nd part of the command
outputfile <- vector()
for(i in locus_names) {
outputfile <- append(outputfile, paste(" -o /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/", i, sep=""))
}
# Length check
length(outputfile) - n
# Get the entire command
commands <- paste(inputfile, outputfile, "-tree &&", sep="")
# Length check
length(commands) - n
# Uncomment to print to file:
# write(commands, "gene-tree-analysis.sh")
#!/bin/bash was then inserted into the first line of the file to convert it into a shell script. The script was run as follows:
chmod 755 /Users/David/Grive/Alfaro_Lab/SortaDate/Locus_analysis/gene-tree-analysis.sh
/Users/David/Grive/Alfaro_Lab/SortaDate/Locus_analysis/gene-tree-analysis.sh
Write a new script that will copy the trees from the nested subdirectories to the same directory where the alignment files are located:
# 1st part of the command
copyfiles <- vector()
for(i in locus_names) {
copyfiles <- append(copyfiles, paste("cp /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/", i, sep = ""))
}
# 2nd part of the command
subdirectories <- vector()
for(i in locus_names) {
subdirectories <- append(subdirectories, paste("-tree/clustalo_default-none-none-raxml_default/", i, sep = ""))
}
# Get the entire command
copying <- paste(copyfiles, subdirectories, ".fasta.final_tree.nw /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/FASTA/ &&", sep="")
# Uncomment to print to file:
# write(copying, "copy-trees.sh")
Write a third script to rename the tree files so that they have the .tre file ending, which SortaDate looks for while searching its target directory:
# 1st part of the command
oldname <- vector()
for(i in locus_names) {
oldname <- append(oldname, paste("mv /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/FASTA/", i, sep = ""))
}
# 2nd part of the command
newname <- vector()
for(i in locus_names) {
newname <- append(newname, paste(".fasta.final_tree.nw /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/FASTA/", i, sep = ""))
}
# Get the entire command:
renametrees <- paste(oldname, newname, ".tre &&", sep = "")
# Uncomment to print to file:
write(renametrees, "rename-trees.sh")
python src/get_var_length.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/FASTA/ --flend .tre --outf Locus_analysis/var-uces --outg alepisaurus_ferox,ceratoscopelus_warmingii
python src/get_bp_genetrees.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/FASTA/ /Users/David/Grive/Alfaro_Lab/Acanthomorpha/12_no_outgroups_scheme_3.tre --flend .tre --outf Locus_analysis/bp-uces
python src/combine_results.py Locus_analysis/var-uces Locus_analysis/bp-uces --outf Locus_analysis/comb-uces
In order of descending priority: bipartition support, root-to-tip variance, tree length
python src/get_good_genes.py Locus_analysis/comb-uces --max 3 --order 3,1,2 --outf Locus_analysis/gg-uces-312
name root-to-tip_var treelength bipartition
uce-1184.tre 0.00510364 5.59114 0.491379310345
uce-383.tre 0.0094896 4.68126 0.465517241379
uce-1317.tre 0.0200846 8.06488 0.465517241379
In order of descending priority: bipartition support, tree length, root-to-tip variance
python src/get_good_genes.py Locus_analysis/comb-uces --max 3 --order 3,2,1 --outf Locus_analysis/gg-uces-321
name root-to-tip_var treelength bipartition
uce-1184.tre 0.00510364 5.59114 0.491379310345
uce-383.tre 0.0094896 4.68126 0.465517241379
uce-1317.tre 0.0200846 8.06488 0.465517241379
In order of descending priority: root-to-tip variance, tree length, bipartition support
python src/get_good_genes.py Locus_analysis/comb-uces --max 3 --order 1,2,3 --outf Locus_analysis/gg-uces-123
name root-to-tip_var treelength bipartition
uce-1075.tre 7.59021e-05 0.363795 0.0689655172414
uce-446.tre 0.000230387 0.897702 0.26724137931
uce-855.tre 0.000309407 1.03296 0.301724137931
In order of descending priority: root-to-tip variance, bipartition support, tree length
python src/get_good_genes.py Locus_analysis/comb-uces --max 3 --order 1,3,2 --outf Locus_analysis/gg-uces-132
name root-to-tip_var treelength bipartition
uce-1075.tre 7.59021e-05 0.363795 0.0689655172414
uce-446.tre 0.000230387 0.897702 0.26724137931
uce-855.tre 0.000309407 1.03296 0.301724137931
In order of descending priority: tree length, root-to-tip variance, bipartition support
python src/get_good_genes.py Locus_analysis/comb-uces --max 3 --order 2,1,3 --outf Locus_analysis/gg-uces-213
name root-to-tip_var treelength bipartition
uce-1075.tre 7.59021e-05 0.363795 0.0689655172414
uce-737.tre 0.000743068 0.814482 0.137931034483
uce-446.tre 0.000230387 0.897702 0.26724137931
In order of descending priority: tree length, bipartition support, root-to-tip variance
python src/get_good_genes.py Locus_analysis/comb-uces --max 3 --order 2,3,1 --outf Locus_analysis/gg-uces-231
name root-to-tip_var treelength bipartition
uce-1075.tre 7.59021e-05 0.363795 0.0689655172414
uce-737.tre 0.000743068 0.814482 0.137931034483
uce-446.tre 0.000230387 0.897702 0.26724137931
Note that the largest, slowest-evolving partition (“ccf55a6ee6d62f840a124bcc0c98ecf5”; 132 kb) was excluded from the first round of analyses for computational reasons. Attempts to analyze it in RAxML after the remaining 31 analyses finished up were unsuccessful.
# Get the 1st part of the command
kmeans_table <- read.table("Set2-kmeans-partitions.txt")
n2 <- length(unlist(kmeans_table))
first <- vector()
for(i in kmeans_table) {
first <- append(first, paste("ete3 build -w standard_raxml -n /Users/David/Grive/Alfaro_Lab/Acanthomorpha/k-means_partitions/", i, sep = ""))
}
# Length check
length(first) - n2
# Remove file endings from the locus names
no_endings <- as.character(as.vector(as.matrix(kmeans_table)))
kmeans_names <- vector()
for(i in no_endings) {
kmeans_names <- append(kmeans_names, sapply(strsplit(i, split='.', fixed=TRUE), function(x) (x[1])))
}
kmeans_names
# Get the 2nd part of the command
second <- vector()
for(i in kmeans_names) {
second <- append(second, paste(" -o /Users/David/Grive/Alfaro_Lab/Acanthomorpha/k-means_partitions/", i, sep=""))
}
second
# Length check
length(second) - n2
# Get the entire command
kmeansscript <- paste(first, second, "-tree &&", sep="")
# Length check
length(kmeansscript) - n2
# Uncomment to print to file:
# write(kmeansscript, "kmeans-analysis.sh")
Copy the trees into the directory containing the alignments:
# 1st part of the command
copytrees <- vector()
for(i in kmeans_names) {
copytrees <- append(copytrees, paste("cp /Users/David/Grive/Alfaro_Lab/Acanthomorpha/k-means_partitions/", i, sep = ""))
}
# 2nd part of the command
locations <- vector()
for(i in kmeans_names) {
locations <- append(locations, paste("-tree/clustalo_default-none-none-raxml_default/", i, sep = ""))
}
# Get the entire command
finalstep <- paste(copytrees, locations, ".phy.fasta.final_tree.nw /Users/David/Grive/Alfaro_Lab/Acanthomorpha/k-means_partitions/FASTA/ &&", sep="")
# Uncomment to print to file:
# write(finalstep, "copy-kmeans-trees.sh")
Change the tree names so that they correspond to the partition names:
# 1st part of the command
oldtreename <- vector()
for(i in kmeans_names) {
oldtreename <- append(oldtreename, paste("mv /Users/David/Grive/Alfaro_Lab/Acanthomorpha/k-means_partitions/FASTA/", i, sep = ""))
}
# 2nd part of the command
newtreename <- vector()
for(i in kmeans_names) {
newtreename <- append(newtreename, paste(".phy.fasta.final_tree.nw /Users/David/Grive/Alfaro_Lab/Acanthomorpha/k-means_partitions/FASTA/", i, sep = ""))
}
# Get the entire command:
changetreenames <- paste(oldtreename, newtreename, ".tre &&", sep = "")
# Uncomment to print to file:
# write(changetreenames, "rename-kmeans-trees.sh")
Finally, rename the partitions:
# 1st part of the command
oldpartition <- vector()
for(i in kmeans_names) {
oldpartition <- append(oldpartition, paste("mv /Users/David/Grive/Alfaro_Lab/Acanthomorpha/k-means_partitions/FASTA/", i, sep = ""))
}
# 2nd part of the command
newpartition <- vector()
for(i in kmeans_names) {
newpartition <- append(newpartition, paste(".phy.fasta /Users/David/Grive/Alfaro_Lab/Acanthomorpha/k-means_partitions/FASTA/", i, sep = ""))
}
# Get the entire command
renamepartitions <- paste(oldpartition, newpartition, ".fasta &&", sep = "")
# Uncomment to print to file:
# write(renamepartitions, "rename-partitions.sh")
python src/get_var_length.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/k-means_partitions/FASTA/ --flend .tre --outf var-kmeans --outg alepisaurus_ferox,ceratoscopelus_warmingii
python src/get_bp_genetrees.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/k-means_partitions/FASTA/ /Users/David/Grive/Alfaro_Lab/Acanthomorpha/12_no_outgroups_scheme_3.tre --flend .tre --outf bp-kmeans
python src/combine_results.py var-kmeans bp-kmeans --outf comb-kmeans
In order of descending priority: bipartition support, root-to-tip variance, tree length
name root-to-tip_var treelength bipartition
8be7c94dcf2d71970c663f6710af40d7.tre 0.0878182 19.8649 0.560344827586
f495e4e0f2f9bbbf091f778067b062f4.tre 0.0230499 11.0301 0.508620689655
0061a6137d1029978876fe13239f57bc.tre 0.0205424 11.2184 0.5
In order of descending priority: bipartition support, tree length, root-to-tip variance
name root-to-tip_var treelength bipartition
8be7c94dcf2d71970c663f6710af40d7.tre 0.0878182 19.8649 0.560344827586
f495e4e0f2f9bbbf091f778067b062f4.tre 0.0230499 11.0301 0.508620689655
0061a6137d1029978876fe13239f57bc.tre 0.0205424 11.2184 0.5
In order of descending priority: root-to-tip variance, tree length, bipartition support
name root-to-tip_var treelength bipartition
4ff6dce0b7cebc9428b4b77e79356484.tre 0.000241636 1.15434 0.0258620689655
589d12bf910a9937d941aa4c836ad87d.tre 0.000935835 2.0223 0.336206896552
fe7aa5d9de0f5f51ef6365ef3b7f1e06.tre 0.00125304 2.60574 0.0603448275862
In order of descending priority: root-to-tip variance, bipartition support, tree length
name root-to-tip_var treelength bipartition
4ff6dce0b7cebc9428b4b77e79356484.tre 0.000241636 1.15434 0.0258620689655
589d12bf910a9937d941aa4c836ad87d.tre 0.000935835 2.0223 0.336206896552
fe7aa5d9de0f5f51ef6365ef3b7f1e06.tre 0.00125304 2.60574 0.0603448275862
In order of descending priority: tree length, root-to-tip variance, bipartition support
name root-to-tip_var treelength bipartition
4ff6dce0b7cebc9428b4b77e79356484.tre 0.000241636 1.15434 0.0258620689655
589d12bf910a9937d941aa4c836ad87d.tre 0.000935835 2.0223 0.336206896552
fe7aa5d9de0f5f51ef6365ef3b7f1e06.tre 0.00125304 2.60574 0.0603448275862
In order of descending priority: tree length, bipartition support, root-to-tip variance
name root-to-tip_var treelength bipartition
4ff6dce0b7cebc9428b4b77e79356484.tre 0.000241636 1.15434 0.0258620689655
589d12bf910a9937d941aa4c836ad87d.tre 0.000935835 2.0223 0.336206896552
fe7aa5d9de0f5f51ef6365ef3b7f1e06.tre 0.00125304 2.60574 0.0603448275862
A bash script was written to automate the following actions:
/Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/FASTA for each locusSplit the alignments for each locus into individual sequences, and these into 50-bp chunks. This step was performed using a custom Python script obtained from http://www.reddit.com/r/bioinformatics/comments/1u8yc7/looking_for_a_script_that_will_split_dna/ceg8rav/?st=j0tbjfco&sh=1e300055.
'''
Splits all sequences within a multi-fasta file into chunks of a specified size.
Fasta header information is retained with each split sequence - its position in
the original is appended to the id. Single-line and multi-line fasta files are
supported. Prints to stout, so pipe to a file to store the result.
Usage:
python splitter.py <filename> <chunksize>
python splitter.py myfile.fa 100
'''
from __future__ import print_function
from sys import argv, version
if version[0] == '2':
from itertools import izip_longest as zl
else:
from itertools import zip_longest as zl
chunksize = int(argv[2])
def writeseq(header, seq):
for i, chunk in enumerate(zl(*[iter(seq)]*chunksize, fillvalue='')):
print(header + '_{}bp'.format(i*chunksize))
print(''.join(chunk))
with open(argv[1]) as f:
header, seq = f.readline().rstrip(), ''
for l in f:
if l[0] != '>':
seq += l.rstrip()
else:
writeseq(header, seq)
header, seq = l.rstrip(), ''
writeseq(header, seq)For each locus, join the individual sequences chunk-wise (i.e., make a single fasta file for all taxa and sites 0 to 50, another one for all taxa and sites 51 to 100, etc.):
library(dplyr)
# Alternating rows (name, sequence, name, sequence) go to two different columns, so that
# each sequence is correctly assigned to its respective taxon:
split_seqs <- read.table("split.txt")
odd <- as.vector(split_seqs[seq(1, nrow(split_seqs), 2), ])
even <- as.vector(split_seqs[seq(2, nrow(split_seqs), 2), ])
odd_name <- "taxon"
even_name <- "sequence"
split_seqs_new <- data.frame(odd, even)
names(split_seqs_new) <- c(odd_name, even_name)
# Determine how long the locus is (i.e., how many 50-bp chunks it has been split into).
# This can be done by counting the number of occurrences of a single taxon name.
# In principle, any name could be used, but since not all of the UCEs include
# all of the taxa, it is advisable to choose a taxon common to all the loci.
n <- length(unique(grep("chaetodon_kleinii", split_seqs_new[,1], value = TRUE)))
# Create a vector of strings that can filter taxon names according to the base pair range
# tag attached to their end
chunks <- vector()
for(i in 0:(n-1)) {
chunks <- append(chunks, paste("_", i*50, "bp", sep = ""))
}
# Create a list of data frames. Each element of the list represents a base pair range
# and consists of a data frame containing both the "taxon" and "sequence" columns of
# split_seqs_new
partition <- list()
for(i in 1:length(chunks)) {
partition[[i]] <- data.frame(filter(split_seqs_new,
grepl(as.character(chunks[i]), taxon)))
}
# Create a matrix whose columns represents individual chunks (i.e., base pair ranges)
# and whose rows have the structure of the original split_seqs data frame -- i.e., name,
# sequence, name, sequence:
chunkmatrix <- matrix(ncol = length(partition),
nrow = 2*(nrow(split_seqs_new)/length(partition)))
for(i in 1:length(partition)) {
for(j in 1:nrow(partition[[i]])) {
chunkmatrix[(2*j - 1), i] <- as.character(partition[[i]][j, "taxon"])
chunkmatrix[2*j, i] <- as.character(partition[[i]][j, "sequence"])
}
}
# Print the resulting fasta files!
for(i in 1:ncol(chunkmatrix)) {
write(chunkmatrix[,i], paste("chunk_", i, ".fasta", sep = ""))
}Add a locus-indicating prefix to all the chunks of a given UCE:
find *.fasta -maxdepth 0 ! -path . -exec mv {} uce-1005_{} \;Copy the resulting fasta files into a single directory.
The contents of the directory were then summarized as follows:
cd /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks/
ls > /Users/David/Grive/Alfaro_Lab/SortaDate/Set3-50bp-chunks.txt
Now, change the taxon names in the chunk FASTA files so that they correspond to the names in the reference tree. First, create a file with the names of all the chunk files:
cd /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks/
ls *.fasta > /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks/chunklist.txt
a <- read.table("/Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks/chunklist.txt")
x <- vector()
for(i in 1:nrow(a)) {
x <- append(x, paste("/Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks/", a[i,], sep = ""))
}
for(i in 1:length(x)) {
c <- read.table(print(x[i]), stringsAsFactors = FALSE)
d <- vector()
for(j in 1:(nrow(c)/2)) {
d[j] <- as.character(c[(2*j-1),])
}
e <- vector()
for(j in 1:length(d)) {
e[j] <- paste(sapply(strsplit(d[j], split="_", fixed=TRUE), function(x) (x[1])),
"_",
sapply(strsplit(d[j], split="_", fixed=TRUE), function(x) (x[2])),
sep = "")
}
for(j in 1:(nrow(c)/2)) {
c[(2*j-1),] <- e[j]
}
write(as.matrix(c), print(x[i]), ncolumns=1)
}
A script was generated to analyze all of the alignment in the directory using RAxML:
chunk_table <- read.table("Set3-50bp-chunks.txt")
newfolders <- vector()
for(i in chunk_table) {
newfolders <- append(newfolders, paste("ete3 build -w standard_raxml -n /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks/", i, sep=""))
}
with_endings <- as.character(as.vector(as.matrix(chunk_table)))
chunk_names <- vector()
for(i in with_endings) {
chunk_names <- append(chunk_names, sapply(strsplit(i, split='.', fixed=TRUE), function(x) (x[1])))
}
tree_location <- vector()
for(i in chunk_names) {
tree_location <- append(tree_location, paste(" -o /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/", i, sep=""))
}
analyzechunks <- paste(newfolders, tree_location, sep="")
write(analyzechunks, "chunk-analysis.sh")
The resulting tree files were then copied into the directory containing the alignments:
copyfrom1 <- vector()
for(i in chunk_names) {
copyfrom1 <- append(copyfrom1, paste("cp /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/", i, sep = ""))
}
copyfrom2 <- vector()
for(i in chunk_names) {
copyfrom2 <- append(copyfrom2, paste("/clustalo_default-none-none-raxml_default/", i, sep = ""))
}
copyto <- paste(copyfrom1, copyfrom2, ".fasta.final_tree.nw /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks/", sep="")
write(copyto, "copy-chunk-trees.sh")
Rename the trees:
brew install rename
rename -S .fasta.final_tree.nw .tre *.fasta.final_tree.nw
Running ls *.fasta | wc -l and ls *.nw | wc -l in the directory showed that out of 1826 chunk FASTA files, no more than 1070 had tree files associated with them.
python src/get_var_length.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks/ --flend .tre --outf Chunk_analysis/var-chunks --outg alepisaurus_ferox,ceratoscopelus_warmingii
python src/get_bp_genetrees.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks/ /Users/David/Grive/Alfaro_Lab/Acanthomorpha/12_no_outgroups_scheme_3.tre --flend .tre --outf Chunk_analysis/bp-chunks
python src/combine_results.py Chunk_analysis/var-chunks Chunk_analysis/bp-chunks --outf Chunk_analysis/comb-chunks
Calculate the Robinson-Foulds distances of the individual chunk trees from the reference tree using the Python script below:
import os, uuid
from ete3 import Tree
t2 = Tree("/Users/David/Grive/Alfaro_Lab/Acanthomorpha/12_no_outgroups_scheme_3.tre")
for file in os.listdir("/Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks/"):
if file.endswith(".tre"):
t1 = Tree(file)
try:
rf = t1.robinson_foulds(t2)
print str(file), (rf[0])
except:
pass
No effect: the minimum observed tree length is non-zero and equal to:
## [1] 8.80081e-05
In order of descending priority: bipartition support, root-to-tip variance, tree length
name root-to-tip_var treelength bipartition
uce-157_chunk_7.tre 0.176994 14.918 0.258620689655
uce-126_chunk_3.tre 0.104056 14.0824 0.224137931034
uce-323_chunk_2.tre 1.16412 48.0104 0.224137931034
## [1] "The Robinson-Foulds distances of the three bets chunks from the reference tree are as follows:"
## chunk rf
## 321 uce-157_chunk_7.tre 164
## chunk rf
## 219 uce-126_chunk_3.tre 166
## chunk rf
## 443 uce-323_chunk_2.tre 168
The chunk with the minimum RF distance from the reference tree:
## chunk rf
## 321 uce-157_chunk_7.tre 164
In order of descending priority: bipartition support, tree length, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-157_chunk_7.tre 0.176994 14.918 0.258620689655
uce-126_chunk_3.tre 0.104056 14.0824 0.224137931034
uce-323_chunk_2.tre 1.16412 48.0104 0.224137931034
In order of descending priority: root-to-tip variance, tree length, bipartition support
name root-to-tip_var treelength bipartition
uce-1243_chunk_4.tre 1.05801e-11 0.0215722 0.0
uce-600_chunk_3.tre 1.28251e-11 0.0205929 0.00862068965517
uce-737_chunk_4.tre 2.16826e-11 0.0215345 0.0
In order of descending priority: root-to-tip variance, bipartition support, tree length
name root-to-tip_var treelength bipartition
uce-1243_chunk_4.tre 1.05801e-11 0.0215722 0.0
uce-600_chunk_3.tre 1.28251e-11 0.0205929 0.00862068965517
uce-737_chunk_4.tre 2.16826e-11 0.0215345 0.0
In order of descending priority: tree length, root-to-tip variance, bipartition support
name root-to-tip_var treelength bipartition
uce-855_chunk_6.tre 3.60737e-11 8.80081e-05 0.0
uce-413_chunk_4.tre 3.51022e-06 0.0205344 0.0
uce-600_chunk_3.tre 1.28251e-11 0.0205929 0.00862068965517
In order of descending priority: tree length, bipartition support, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-855_chunk_6.tre 3.60737e-11 8.80081e-05 0.0
uce-413_chunk_4.tre 3.51022e-06 0.0205344 0.0
uce-600_chunk_3.tre 1.28251e-11 0.0205929 0.00862068965517
Copy all the tree files to a new directory:
cd /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa
mkdir Chunks_75
cp Chunks/*.tre Chunks_75Collapse all the nodes with SH-like support values of less than 75% using the following Python script:
import os, uuid
from ete3 import Tree
for file in os.listdir("/Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks"):
if file.endswith(".tre"):
outname = "/Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks_75/" + str(file)
t = Tree(file, format=0)
print t.get_ascii(attributes=['support', 'name'])
for node in t.get_descendants():
if not node.is_leaf() and node.support <= 0.75:
node.delete()
print t.get_ascii(attributes=['support', 'name'])
t.write(format=0, outfile=outname)Copy the fasta alignments to the same directory:
cp Chunks/*.fasta Chunks_75python src/get_var_length.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks_75/ --flend .tre --outf Chunk75_analysis/var-chunks75 --outg alepisaurus_ferox,ceratoscopelus_warmingii
python src/get_bp_genetrees.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks_75/ /Users/David/Grive/Alfaro_Lab/Acanthomorpha/12_no_outgroups_scheme_3.tre --flend .tre --outf Chunk75_analysis/bp-chunks75
python src/combine_results.py Chunk75_analysis/var-chunks75 Chunk75_analysis/bp-chunks75 --outf Chunk75_analysis/comb-chunks75
A script was written to delete all lines containing NAs, as well as all lines that only contained an entry for bipartition support but none for root-to-tip branch length variance or tree length. This was accomplished by filling these partially empty lines with NAs in the first step and deleting them in the second step:
combchunks75 <- read.table("/Users/David/Grive/Alfaro_Lab/SortaDate/Chunk75_analysis/comb-chunks75", fill = TRUE)
filtered <- na.omit(combchunks75)
write.table(filtered,
"/Users/David/Grive/Alfaro_Lab/SortaDate/Chunk75_analysis/comb-chunks75-filtered",
sep = "\t",
quote = FALSE,
row.names = FALSE,
col.names = FALSE)
The length of the filtered comb file is 901 lines, meaning that SortaDate failed to obtain root-to-tip variance and/or tree length for 169 trees.
No effect: the minimum observed tree length is non-zero and equal to:
## [1] 0.0202802
Note that this result is orders of magnitude larger than those observed in the other five chunk datasets.
In order of descending priority: bipartition support, root-to-tip variance, tree length
name root-to-tip_var treelength bipartition
uce-157_chunk_7.tre 0.163848 13.508 0.198275862069
uce-126_chunk_3.tre 0.101031 13.9331 0.181034482759
uce-323_chunk_2.tre 0.459664 40.9379 0.146551724138
## [1] "The Robinson-Foulds distances of the three best chunks from the reference tree are as follows:"
## chunk rf
## 284 uce-157_chunk_7.tre 126
## chunk rf
## 194 uce-126_chunk_3.tre 114
## chunk rf
## 391 uce-323_chunk_2.tre 125
The chunk with the minimum RF distance from the reference tree:
## chunk rf
## 189 uce-1263_chunk_2.tre 106
In order of descending priority: bipartition support, tree length, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-157_chunk_7.tre 0.163848 13.508 0.198275862069
uce-126_chunk_3.tre 0.101031 13.9331 0.181034482759
uce-323_chunk_2.tre 0.459664 40.9379 0.146551724138
In order of descending priority: root-to-tip variance, tree length, bipartition support
name root-to-tip_var treelength bipartition
uce-815_chunk_8.tre 0.0 0.0202802 0.0
uce-359_chunk_7.tre 0.0 0.0203467 0.00862068965517
uce-160_chunk_3.tre 0.0 0.0204413 0.0
In order of descending priority: root-to-tip variance, bipartition support, tree length
name root-to-tip_var treelength bipartition
uce-200_chunk_2.tre 0.0 0.0317025 0.0344827586207
uce-129_chunk_5.tre 0.0 0.0211935 0.0258620689655
uce-1062_chunk_5.tre 0.0 0.113443 0.0258620689655
In order of descending priority: tree length, root-to-tip variance, bipartition support
name root-to-tip_var treelength bipartition
uce-815_chunk_8.tre 0.0 0.0202802 0.0
uce-359_chunk_7.tre 0.0 0.0203467 0.00862068965517
uce-160_chunk_3.tre 0.0 0.0204413 0.0
In order of descending priority: tree length, bipartition support, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-815_chunk_8.tre 0.0 0.0202802 0.0
uce-359_chunk_7.tre 0.0 0.0203467 0.00862068965517
uce-160_chunk_3.tre 0.0 0.0204413 0.0
(The first three steps were identical to those described above.)
python src/get_var_length.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks_90/ --flend .tre --outf Chunk90_analysis/var-chunks90 --outg alepisaurus_ferox,ceratoscopelus_warmingii
python src/get_bp_genetrees.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-114-taxa/Chunks_90/ /Users/David/Grive/Alfaro_Lab/Acanthomorpha/12_no_outgroups_scheme_3.tre --flend .tre --outf Chunk90_analysis/bp-chunks90
python src/combine_results.py Chunk90_analysis/var-chunks90 Chunk90_analysis/bp-chunks90 --outf Chunk90_analysis/comb-chunks90
Delete the lines with NAs:
combchunks90 <- read.table("/Users/David/Grive/Alfaro_Lab/SortaDate/Chunk90_analysis/comb-chunks90", fill = TRUE)
filtered <- na.omit(combchunks90)
write.table(filtered,
"/Users/David/Grive/Alfaro_Lab/SortaDate/comb-chunks90-filtered",
sep = "\t",
quote = FALSE,
row.names = FALSE,
col.names = FALSE)
The length of the filtered comb file is 536 lines, meaning that SortaDate failed to obtain root-to-tip variance and/or tree length for 534 (almost 50%) trees.
No effect: the minimum observed tree length is non-zero and equal to:
## [1] 8.71304e-07
In order of descending priority: bipartition support, root-to-tip variance, tree length
name root-to-tip_var treelength bipartition
uce-1317_chunk_8.tre 0.0166339 15.2225 0.0689655172414
uce-120_chunk_6.tre 0.187599 4.6936 0.0689655172414
uce-323_chunk_2.tre 0.325194 35.4206 0.0689655172414
## [1] "The Robinson-Foulds distances of the three best chunks from the reference tree are as follows:"
## chunk rf
## 159 uce-1317_chunk_8.tre 111
## chunk rf
## 81 uce-120_chunk_6.tre 114
## chunk rf
## 248 uce-323_chunk_2.tre 107
The chunk with the minimum RF distance from the reference tree:
## chunk rf
## 235 uce-267_chunk_7.tre 106
In order of descending priority: bipartition support, tree length, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-120_chunk_6.tre 0.187599 4.6936 0.0689655172414
uce-1317_chunk_8.tre 0.0166339 15.2225 0.0689655172414
uce-323_chunk_2.tre 0.325194 35.4206 0.0689655172414
In order of descending priority: root-to-tip variance, tree length, bipartition support
name root-to-tip_var treelength bipartition
uce-537_chunk_3.tre 0.0 8.71304e-07 0.00862068965517
uce-84_chunk_5.tre 0.0 9.84452e-07 0.0172413793103
uce-739_chunk_2.tre 0.0 2.2254e-06 0.0344827586207
In order of descending priority: root-to-tip variance, bipartition support, tree length
name root-to-tip_var treelength bipartition
uce-739_chunk_2.tre 0.0 2.2254e-06 0.0344827586207
uce-832_chunk_4.tre 0.0 1.05876 0.0258620689655
uce-84_chunk_5.tre 0.0 9.84452e-07 0.0172413793103
In order of descending priority: tree length, root-to-tip variance, bipartition support
name root-to-tip_var treelength bipartition
uce-537_chunk_3.tre 0.0 8.71304e-07 0.00862068965517
uce-84_chunk_5.tre 0.0 9.84452e-07 0.0172413793103
uce-739_chunk_2.tre 0.0 2.2254e-06 0.0344827586207
In order of descending priority: tree length, bipartition support, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-537_chunk_3.tre 0.0 8.71304e-07 0.00862068965517
uce-84_chunk_5.tre 0.0 9.84452e-07 0.0172413793103
uce-739_chunk_2.tre 0.0 2.2254e-06 0.0344827586207
A bash script used to split the UCE loci into 50-bp chunks was identical to that used above, with the exception of step 3 (joining all taxa represented by a given chunk into a new FASTA file), for which the following code was used:
library(dplyr)
# Alternating rows (name, sequence, name, sequence) go to two different columns, so that
# each sequence is correctly assigned to its respective taxon:
split_seqs <- read.table("split.txt")
odd <- as.vector(split_seqs[seq(1, nrow(split_seqs), 2), ])
even <- as.vector(split_seqs[seq(2, nrow(split_seqs), 2), ])
odd_name <- "taxon"
even_name <- "sequence"
split_seqs_new <- data.frame(odd, even)
names(split_seqs_new) <- c(odd_name, even_name)
# Determine how long the locus is (i.e., how many 50-bp chunks it has been split into).
# This can be done by counting the number of occurrences of a single taxon name.
# In principle, any name could be used, but since no taxon appears to be common to all
# the loci, the code below grabs the first taxon name appearing in the fasta file and
# counts its occurrences.
m <- paste(sapply(strsplit(as.character(split_seqs_new[1,1]), split="_", fixed=TRUE),
function(x) (x[1])),
"_", sapply(strsplit(as.character(split_seqs_new[1,1]), split="_", fixed=TRUE), function(x) (x[2])),
sep = "")
n <- length(unique(grep(m, split_seqs_new[,1], value = TRUE)))
# Create a vector of strings that can filter taxon names according to the base pair range
# tag attached to their end
chunks <- vector()
for(i in 0:(n-1)) {
chunks <- append(chunks, paste("_", i*50, "bp", sep = ""))
}
# Create a list of data frames. Each element of the list represents a base pair range
# and consists of a data frame containing both the "taxon" and "sequence" columns of
# split_seqs_new
partition <- list()
for(i in 1:length(chunks)) {
partition[[i]] <- data.frame(filter(split_seqs_new,
grepl(as.character(chunks[i]), taxon)))
}
# Create a matrix whose columns represents individual chunks (i.e., base pair ranges)
# and whose rows have the structure of the original split_seqs data frame -- i.e., name,
# sequence, name, sequence:
chunkmatrix <- matrix(ncol = length(partition),
nrow = 2*(nrow(split_seqs_new)/length(partition)))
for(i in 1:length(partition)) {
for(j in 1:nrow(partition[[i]])) {
chunkmatrix[(2*j - 1), i] <- as.character(partition[[i]][j, "taxon"])
chunkmatrix[2*j, i] <- as.character(partition[[i]][j, "sequence"])
}
}
# Print the resulting fasta files!
for(i in 1:ncol(chunkmatrix)) {
write(chunkmatrix[,i], paste("chunk_", i, ".fasta", sep = ""))
}
The taxon names in the chunk FASTA files were then stripped of the chunk-indicating suffixes so as to correspond with the names used in the reference tree, and a script was used to analyze all the chunks using RAxML. The resulting trees were then copied into the directory containing the chunks. Out of 6543 chunk FASTA files, only 4237 had tree files associated with them, suggesting that RAxML failed to infer a tree for a given chunk in approx. 35% of cases.
python src/get_var_length.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-90-taxa/Chunks/ --flend .tre --outf Min-90-chunk_analysis/var-min-90-chunks --outg alepisaurus_ferox,ceratoscopelus_warmingii
Running the script produced 1029 warnings about either Alepisaurus ferox or Ceratoscopelus warmingii missing from the chunk tree. Despite this, the tree length and root-to-tip variance was computed for all the 4237 trees.
python src/get_bp_genetrees.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-90-taxa/Chunks/ /Users/David/Grive/Alfaro_Lab/Acanthomorpha/12_no_outgroups_scheme_3.tre --flend .tre --outf Min-90-chunk_analysis/bp-min-90-chunks
python src/combine_results.py Min-90-chunk_analysis/var-min-90-chunks Min-90-chunk_analysis/bp-min-90-chunks --outf Min-90-chunk_analysis/comb-min-90-chunks
In order of descending priority: bipartition support, root-to-tip variance, tree length
name root-to-tip_var treelength bipartition
uce-157_chunk_7.tre 0.176994 14.918 0.258620689655
uce-1244_chunk_1.tre 0.0534134 11.1586 0.224137931034
uce-126_chunk_3.tre 0.104056 14.0824 0.224137931034
## [1] "The Robinson-Foulds distances of the three best chunks from the reference tree are as follows:"
## chunk rf
## 1229 uce-157_chunk_7.tre 164
## chunk rf
## 822 uce-1244_chunk_1.tre 158
## chunk rf
## 907 uce-126_chunk_3.tre 166
The chunk with the minimum RF distance from the reference tree:
## chunk rf
## 6 uce-1000_chunk_6.tre 136
In order of descending priority: bipartition support, tree length, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-157_chunk_7.tre 0.176994 14.918 0.258620689655
uce-1244_chunk_1.tre 0.0534134 11.1586 0.224137931034
uce-126_chunk_3.tre 0.104056 14.0824 0.224137931034
In order of descending priority: root-to-tip variance, tree length, bipartition support
name root-to-tip_var treelength bipartition
uce-956_chunk_5.tre 2.3002e-12 8.56621e-05 0.0
uce-393_chunk_3.tre 5.54758e-12 9.22463e-05 0.0
uce-921_chunk_4.tre 5.8998e-12 7.89216e-05 0.0
In order of descending priority: root-to-tip variance, bipartition support, tree length
name root-to-tip_var treelength bipartition
uce-956_chunk_5.tre 2.3002e-12 8.56621e-05 0.0
uce-393_chunk_3.tre 5.54758e-12 9.22463e-05 0.0
uce-921_chunk_4.tre 5.8998e-12 7.89216e-05 0.0
In order of descending priority: tree length, root-to-tip variance, bipartition support
name root-to-tip_var treelength bipartition
uce-921_chunk_4.tre 5.8998e-12 7.89216e-05 0.0
uce-1173_chunk_3.tre 1.96604e-11 8.52591e-05 0.0
uce-956_chunk_5.tre 2.3002e-12 8.56621e-05 0.0
In order of descending priority: tree length, bipartition support, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-921_chunk_4.tre 5.8998e-12 7.89216e-05 0.0
uce-1173_chunk_3.tre 1.96604e-11 8.52591e-05 0.0
uce-956_chunk_5.tre 2.3002e-12 8.56621e-05 0.0
No effect: the minimum observed tree length is non-zero and equal to:
## [1] 7.89216e-05
## Number of trees after filtering: 4237
## Percentage of trees after filtering: 100%
## Original min. tree length: 7.89216e-05
## Original max. tree length: 214.974
## Min. tree length after step 2: 0.285338
## Max. tree length after step 2: 24.1424
## Number of trees after step 2: 3389
## Percentage of trees after step 2: 79.98584%
## Max. root-to-tip variance from step 2: 2.72349
## Max. root-to-tip variance after step 3: 0.00905244
## Number of trees after step 3: 2270
## Percentage of trees after step 3: 53.57564%
## Min. bipartition support from step 3: 0
## Min. bipartition support after step 4: 0.1034483
## Number of trees after step 4: 170
## Percentage of trees after step 4: 4.012273%
The nodes with SH-like support values below 75% were collapsed using the script above. The corresponding FASTA files were copied into the resulting directory, and SortaDate was run as follows:
python src/get_var_length.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-90-taxa/Chunks_75/ --flend .tre --outf Min-90-chunk75_analysis/var-min-90-chunks75 --outg alepisaurus_ferox,ceratoscopelus_warmingii
Running the script resulted in occasional segmentation faults as well as “this really only works with nexus or newick” warning messages. The resulting file had the full number of lines (4237: one for each tree), but some of them were blank and others contained NAs.
python src/get_bp_genetrees.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-90-taxa/Chunks_75/ /Users/David/Grive/Alfaro_Lab/Acanthomorpha/12_no_outgroups_scheme_3.tre --flend .tre --outf Min-90-chunk75_analysis/bp-min-90-chunks75
python src/combine_results.py Min-90-chunk75_analysis/var-min-90-chunks75 Min-90-chunk75_analysis/bp-min-90-chunks75 --outf Min-90-chunk75_analysis/comb-min-90-chunks75
Delete the lines with NAs:
comb90chunks75 <- read.table("/Users/David/Grive/Alfaro_Lab/SortaDate/Min-90-chunk75_analysis/comb-min-90-chunks75", fill = TRUE)
filtered <- na.omit(comb90chunks75)
write.table(filtered,
"/Users/David/Grive/Alfaro_Lab/SortaDate/Min-90-chunk75_analysis/comb-min-90-chunks75-filtered",
sep = "\t",
quote = FALSE,
row.names = FALSE,
col.names = FALSE)
nrow(filtered)
The length of the filtered comb file is 3589 lines, meaning that SortaDate failed to obtain root-to-tip variance and/or tree length for 648 trees.
In order of descending priority: bipartition support, root-to-tip variance, tree length
name root-to-tip_var treelength bipartition
uce-157_chunk_7.tre 0.163848 13.508 0.198275862069
uce-1244_chunk_1.tre 0.040797 10.3002 0.181034482759
uce-126_chunk_3.tre 0.101031 13.9331 0.181034482759
## [1] "The Robinson-Foulds distances of the three best chunks from the reference tree are as follows:"
## chunk rf
## 1064 uce-157_chunk_7.tre 126
## chunk rf
## 710 uce-1244_chunk_1.tre 111
## chunk rf
## 788 uce-126_chunk_3.tre 114
The chunk with the minimum RF distance from the reference tree:
## chunk rf
## 2016 uce-463_chunk_2.tre 85
In order of descending priority: bipartition support, tree length, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-157_chunk_7.tre 0.163848 13.508 0.198275862069
uce-1244_chunk_1.tre 0.040797 10.3002 0.181034482759
uce-126_chunk_3.tre 0.101031 13.9331 0.181034482759
In order of descending priority: root-to-tip variance, tree length, bipartition support
name root-to-tip_var treelength bipartition
uce-1175_chunk_4.tre 0.0 8.48388e-07 0.00862068965517
uce-547_chunk_2.tre 0.0 1.18678e-06 0.0172413793103
uce-815_chunk_8.tre 0.0 0.0202802 0.0
In order of descending priority: root-to-tip variance, bipartition support, tree length
name root-to-tip_var treelength bipartition
uce-67_chunk_4.tre 0.0 0.0214551 0.0603448275862
uce-200_chunk_2.tre 0.0 0.0317025 0.0344827586207
uce-525_chunk_3.tre 0.0 0.020706 0.0258620689655
In order of descending priority: tree length, root-to-tip variance, bipartition support
name root-to-tip_var treelength bipartition
uce-1175_chunk_4.tre 0.0 8.48388e-07 0.00862068965517
uce-547_chunk_2.tre 0.0 1.18678e-06 0.0172413793103
uce-815_chunk_8.tre 0.0 0.0202802 0.0
In order of descending priority: tree length, bipartition support, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-1175_chunk_4.tre 0.0 8.48388e-07 0.00862068965517
uce-547_chunk_2.tre 0.0 1.18678e-06 0.0172413793103
uce-815_chunk_8.tre 0.0 0.0202802 0.0
No effect: the minimum observed tree length is non-zero and equal to:
## [1] 8.48388e-07
## Number of trees after filtering: 3589
## Percentage of trees after filtering: 100%
## Original min. tree length: 8.48388e-07
## Original max. tree length: 209.395
## Min. tree length after step 2: 0.124261
## Max. tree length after step 2: 19.679
## Number of trees after step 2: 2871
## Percentage of trees after step 2: 79.99443%
## Max. root-to-tip variance from step 2: 6.66986
## Max. root-to-tip variance after step 3: 0.00645959
## Number of trees after step 3: 1923
## Percentage of trees after step 3: 53.58038%
## Min. bipartition support from step 3: 0
## Min. bipartition support after step 4: 0.06034483
## Number of trees after step 4: 169
## Percentage of trees after step 4: 4.708833%
python src/get_var_length.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-90-taxa/Chunks_90/ --flend .tre --outf Min-90-chunk90_analysis/var-min-90-chunks90 --outg alepisaurus_ferox,ceratoscopelus_warmingii
As in the previous case, the command led to a number of segfaults and “this really only works with nexus or newick” warnings, corresponding to lines in the resulting file that were either incomplete or included NAs.
python src/get_bp_genetrees.py /Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-90-taxa/Chunks_90/ /Users/David/Grive/Alfaro_Lab/Acanthomorpha/12_no_outgroups_scheme_3.tre --flend .tre --outf Min-90-chunk90_analysis/bp-min-90-chunks90
python src/combine_results.py Min-90-chunk90_analysis/var-min-90-chunks90 Min-90-chunk90_analysis/bp-min-90-chunks90 --outf Min-90-chunk90_analysis/comb-min-90-chunks90
Delete the lines with NAs:
comb90chunks90 <- read.table("/Users/David/Grive/Alfaro_Lab/SortaDate/Min-90-chunk90_analysis/comb-min-90-chunks90", fill = TRUE)
filtered <- na.omit(comb90chunks90)
write.table(filtered,
"/Users/David/Grive/Alfaro_Lab/SortaDate/Min-90-chunk90_analysis/comb-min-90-chunks90-filtered",
sep = "\t",
quote = FALSE,
row.names = FALSE,
col.names = FALSE)
nrow(filtered)
The length of the filtered comb file is 2319 lines, meaning that SortaDate failed to obtain root-to-tip variance and/or tree length for 1918 trees.
In order of descending priority: bipartition support, root-to-tip variance, tree length
name root-to-tip_var treelength bipartition
uce-625_chunk_10.tre 0.0675117 0.706842 0.0775862068966
uce-1317_chunk_8.tre 0.0166339 15.2225 0.0689655172414
uce-120_chunk_6.tre 0.187599 4.6936 0.0689655172414
## [1] "The Robinson-Foulds distances of the three best chunks from the reference tree are as follows:"
## chunk rf
## 1686 uce-625_chunk_10.tre 106
## chunk rf
## 629 uce-1317_chunk_8.tre 111
## chunk rf
## 429 uce-120_chunk_6.tre 114
The chunk with the minimum RF distance from the reference tree:
## chunk rf
## 678 uce-1340_chunk_2.tre 85
In order of descending priority: bipartition support, tree length, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-625_chunk_10.tre 0.0675117 0.706842 0.0775862068966
uce-120_chunk_6.tre 0.187599 4.6936 0.0689655172414
uce-1317_chunk_8.tre 0.0166339 15.2225 0.0689655172414
In order of descending priority: root-to-tip variance, tree length, bipartition support
name root-to-tip_var treelength bipartition
uce-1175_chunk_4.tre 0.0 8.48388e-07 0.00862068965517
uce-675_chunk_5.tre 0.0 8.58914e-07 0.0
uce-537_chunk_3.tre 0.0 8.71304e-07 0.00862068965517
In order of descending priority: root-to-tip variance, bipartition support, tree length
name root-to-tip_var treelength bipartition
uce-1189_chunk_2.tre 0.0 1.52836 0.0431034482759
uce-739_chunk_2.tre 0.0 2.2254e-06 0.0344827586207
uce-67_chunk_4.tre 0.0 0.0214551 0.0344827586207
In order of descending priority: tree length, root-to-tip variance, bipartition support
name root-to-tip_var treelength bipartition
uce-1175_chunk_4.tre 0.0 8.48388e-07 0.00862068965517
uce-675_chunk_5.tre 0.0 8.58914e-07 0.0
uce-537_chunk_3.tre 0.0 8.71304e-07 0.00862068965517
In order of descending priority: tree length, bipartition support, root-to-tip variance
name root-to-tip_var treelength bipartition
uce-1175_chunk_4.tre 0.0 8.48388e-07 0.00862068965517
uce-675_chunk_5.tre 0.0 8.58914e-07 0.0
uce-537_chunk_3.tre 0.0 8.71304e-07 0.00862068965517
No effect: the minimum observed tree length is non-zero and equal to:
## [1] 8.48388e-07
## Number of trees after filtering: 2319
## Percentage of trees after filtering: 100%
## Original min. tree length: 8.48388e-07
## Original max. tree length: 141.225
## Min. tree length after step 2: 0.111172
## Max. tree length after step 2: 20.8823
## Number of trees after step 2: 1855
## Percentage of trees after step 2: 79.99138%
## Max. root-to-tip variance from step 2: 40.1753
## Max. root-to-tip variance after step 3: 0.00177898
## Number of trees after step 3: 1243
## Percentage of trees after step 3: 53.60069%
## Min. bipartition support from step 3: 0
## Min. bipartition support after step 4: 0.03448276
## Number of trees after step 4: 66
## Percentage of trees after step 4: 2.846054%
Out of the 66 chunks selected from the SH-like > 0.9 dataset, 65 were 50-bp long. To facilitate PartitionFinder searches, these chunks were aligned first using SequenceMatrix v1.8, with the last (47-bp) chunk attached to the resulting alignment afterwards. This made it easier to automate the calculation of base pair ranges for each locus (the latter needed to be included in the PartitionFinder configuration file). The outgroups (Alepisaurus ferox and Ceratoscopelus warmingii) were excluded from the concatenated alignment, as they were not present in the available topology constraint.
PartitionFinder was first run with the “rcluster” search option. Although the “models” option was set to “all”, since rcluster searches can only be performed in RAxML, this effectively limited the analysis to the three models implemented in the latter software (GTR, GTR+\(\Gamma\), GTR+\(\Gamma\)+I). This run yielded 8 subsets of the following properties:
| Subset | Best Model | Sites | Rate under GTR+\(\Gamma\) | ID |
|---|---|---|---|---|
| 1 | GTR+\(\Gamma\) | 1047 | 1.048826 | 76dec7513c9b37738bfac30d5d512cf3 |
| 2 | GTR+I+\(\Gamma\) | 1100 | 0.672610 | 111ec939b8c23b0e4e2529cee50f387a |
| 3 | GTR+\(\Gamma\) | 550 | 0.281570 | 00fea7ffef92b99d78df6650b56ebb0c |
| 4 | GTR+\(\Gamma\) | 100 | 0.965800 | 065a20f09eb1fa7383dedb55df51e100 |
| 5 | GTR+\(\Gamma\) | 200 | 2.278670 | a6afbf36fc27e68c089e09c31ca3c71f |
| 6 | GTR+\(\Gamma\) | 50 | 1.682760 | da54ec6f6966e6b696433ef124df9e1f |
| 7 | GTR+I+\(\Gamma\) | 50 | 2.739838 | 2d0d981211d9b6e3f364c1fcd221b555 |
| 8 | GTR+\(\Gamma\) | 200 | 1.356237 | fd9894d33a357dcdab10c55c5153eb16 |
In the next step, the alignment was analyzed under the “greedy” search option, with the BEAST model set and without the “–raxml” flag. This search recovered the following 14 partitions:
| Subset | Best Model | Sites | Tree size under the best model | ID |
|---|---|---|---|---|
| 1 | HKY+\(\Gamma\)+X | 100 | 6.07552 | 3f353939a600a6973a6a1e0608925da6 |
| 2 | TRNEF+\(\Gamma\) | 200 | 4.34326 | ceefdb63d1de1122404ab11285f05f27 |
| 3 | JC+\(\Gamma\) | 50 | 2.80253 | ac2a21040350ec54b4664bc752a13a45 |
| 4 | TRNEF+\(\Gamma\) | 300 | 1.53147 | baed377a41689244a752bc9e3169c975 |
| 5 | GTR+I+\(\Gamma\)+X | 350 | 3.44224 | ce05da9c4ae84b4a74cb468b18fcda82 |
| 6 | K80+I+\(\Gamma\) | 450 | 3.71596 | 16b9f71868c90950d0dd144fd5de511f |
| 7 | SYM+\(\Gamma\) | 300 | 8.15389 | c5076d62640bd9eeabe53de6e5bf7f7a |
| 8 | K80+\(\Gamma\) | 150 | 9.20524 | d5aeca1d1fad19fc89dbb336bad98cbd |
| 9 | K80+\(\Gamma\) | 497 | 5.13898 | e7f992b256534832ccdb46c09aa6e9a4 |
| 10 | HKY+\(\Gamma\)+X | 300 | 2.87180 | 4e722846245f8cd6600c32f3131120a6 |
| 11 | TRNEF+\(\Gamma\) | 250 | 5.87708 | 72ad9849e1f3be2cbffa1097935e00fc |
| 12 | GTR+I+\(\Gamma\)+X | 50 | 36.99851 | 2d0d981211d9b6e3f364c1fcd221b555 |
| 13 | JC+\(\Gamma\) | 150 | 1.20339 | d687ea29d8ab88ed27334eafc8164ec8 |
| 14 | TRNEF+\(\Gamma\) | 150 | 13.44953 | 00f862e3cabdf1be1b99bc5b1455ba78 |
(In PhyML, “tree size” denotes the sum of edge lengths: see http://github.com/stephaneguindon/phyml/blob/master/doc/phyml-manual.tex. “X” denotes estimated, as opposed to empirical, base frequencies.)
The two 50-bp partitions were removed. Each partition received its own substitution model, which was generally identical to that selected by PartitionFinder, except for those models that contained the I parameter (proportion of invariant sites). Since this parameter and \(\Gamma\) account for the same phenomenon (rate heterogeneity across sites), their simultaneous inclusion causes the resulting model to be non-identifiable, leading to potential mixing problems in MCMC simulations. The models that were not directly available in BEAUTi were implemented as follows:
Each \(\Gamma\) rate heterogeneity distribution was discretized into 4 categories. The default improper prior on the relative rates (the allMus parameter) was set to a gamma distribution with a shape of 0.001 and a scale of 1000 following the recommendations at https://www.biostat.washington.edu/sites/default/files/modules//2016_SISMID_13_11.pdf. All the priors on substitution model parameters were kept at their default values.
In contrast to the substitution models, the parameters of the uncorrelated lognormal clock model were linked across partitions. The ucld.mean hyperparameter was assigned a lognormal hyperprior with a mean of 0.005 (in real space) and a log standard deviation of 1, with the initial value set equal to 0.005. A truncated exponential hyperprior with support (0, 1), a mean of 0.3, and an initial value of 0.1 was used for ucld.stdev.
The “fixed tree topology” operator mix was used (based on a user-supplied topology common to all partitions), with the tuning of the ucld.mean and ucld.stdev operators set to 0.9 (default value = 0.75) and their weight doubled (from 3.0 to 6.0). Default tuning values and weights were used for all the remaining operators.
The 12 internal calibrations were all implemented as exponentials, and the (Polymixia + Aphredoderus) received the corrected calibration whose 95th percentile was equal to 116.35 Ma. A truncated exponential distribution supported on (98 Ma, 143 Ma) was constructed to calibrate the root of the tree, with its mean equal to the midpoint of the support interval (22.5 without the offset).
Finally, the MCMC simulation was run for 500 million generations, with a sampling period of 1000 generations.
beast -threads 12 -beagle_SSE concatchunks.xml
Two more analyses were performed using BEAST; their settings were identical to those described above except for the clock model used. In both cases, a random local clock model was utilized, with one analysis using the uncorrelated version of the model (with the ratesAreMultipliers option in the XML file set to the default value of "false") and the other employing the correlated version (ratesAreMultipliers="true"). In both analyses, default priors were placed on the parameters of the clock model.
beast -threads 6 -beagle_SSE concatchunks-urlc.xml
beast -threads 6 -beagle_SSE concatchunks-crlc.xml
The analysis finished after 12.597 days and the resulting log file was examined using LogAnalyser to determine the EES value of each parameter:
java -Xmx6000m -cp /home/analysis/.linuxbrew/lib/beast.jar dr.app.tools.LogAnalyser -burnin 50000000 -ess concatchunks.log
## statistic median ESS
## 1 posterior -48625.69180000 3413.0600
## 2 prior -2049.74610000 844.2895
## 3 likelihood -46575.83370000 32241.1992
## 4 treeModel.rootHeight 137.72570000 2238.3418
## 5 tmrca(Aipichthys) 120.61650000 1324.2513
## 6 tmrca(Berybolcensis) 55.65810000 6036.6497
## 7 tmrca(Calatomus) 23.59360000 718.9452
## 8 tmrca(Chaetodontidae) 37.91700000 6567.4848
## 9 tmrca(Eastmanalepes) 50.96060000 16986.7366
## 10 tmrca(Eobuglossus) 43.01970000 8226.7418
## 11 tmrca(Eucoelopoma) 56.77560000 5699.0608
## 12 tmrca(Homonotichthys) 98.71850000 1207.3403
## 13 tmrca(Mcconichthys) 66.97340000 688.1744
## 14 tmrca(Mene) 59.19900000 4229.6624
## 15 tmrca(Ramphexocoetus) 53.71620000 3420.8121
## 16 tmrca(Root) 137.72570000 2238.3418
## 17 tmrca(Tarkus) 49.83070000 13887.1125
## 18 birthDeath.meanGrowthRate 0.01870000 3785.5560
## 19 birthDeath.relativeDeathRate 0.05030000 125120.0000
## 20 Subset1HKYGX.kappa 3.30330000 336740.0000
## 21 Subset1HKYGX.frequencies1 0.38220000 160000.0000
## 22 Subset1HKYGX.frequencies2 0.23020000 194740.0000
## 23 Subset1HKYGX.frequencies3 0.15200000 188220.0000
## 24 Subset1HKYGX.frequencies4 0.23390000 191870.0000
## 25 Subset1HKYGX.alpha 0.98970000 344070.0000
## 26 Subset2TRNEFG.kappa1 4.82390000 339050.0000
## 27 Subset2TRNEFG.kappa2 1.81510000 353590.0000
## 28 Subset2TRNEFG.alpha 0.67370000 343600.0000
## 29 Subset3TRNEFG.kappa1 3.05060000 330620.0000
## 30 Subset3TRNEFG.kappa2 4.86070000 327930.0000
## 31 Subset3TRNEFG.alpha 0.46710000 335870.0000
## 32 Subset4GTRIGX.ac 0.17860000 142440.0000
## 33 Subset4GTRIGX.ag 0.41800000 69343.9763
## 34 Subset4GTRIGX.at 0.10600000 158030.0000
## 35 Subset4GTRIGX.cg 0.34480000 106840.0000
## 36 Subset4GTRIGX.gt 0.14930000 132440.0000
## 37 Subset4GTRIGX.frequencies1 0.30180000 109180.0000
## 38 Subset4GTRIGX.frequencies2 0.21230000 87820.5730
## 39 Subset4GTRIGX.frequencies3 0.31520000 91691.2293
## 40 Subset4GTRIGX.frequencies4 0.16960000 98336.1071
## 41 Subset4GTRIGX.alpha 0.44590000 276290.0000
## 42 Subset5K80IG.kappa 3.56400000 364980.0000
## 43 Subset5K80IG.alpha 0.31900000 370210.0000
## 44 Subset6SYMG.ac 0.31450000 233040.0000
## 45 Subset6SYMG.ag 1.61400000 171440.0000
## 46 Subset6SYMG.at 0.24760000 241230.0000
## 47 Subset6SYMG.cg 1.23840000 197650.0000
## 48 Subset6SYMG.gt 0.24490000 285190.0000
## 49 Subset6SYMG.alpha 0.52120000 361230.0000
## 50 Subset7K80G.kappa 2.93730000 365130.0000
## 51 Subset7K80G.alpha 0.70830000 361470.0000
## 52 Subset8K80G.kappa 3.17480000 373600.0000
## 53 Subset8K80G.alpha 1.22040000 364000.0000
## 54 Subset9HKYGX.kappa 3.59770000 358280.0000
## 55 Subset9HKYGX.frequencies1 0.20910000 185070.0000
## 56 Subset9HKYGX.frequencies2 0.25900000 189260.0000
## 57 Subset9HKYGX.frequencies3 0.30580000 174970.0000
## 58 Subset9HKYGX.frequencies4 0.22510000 192860.0000
## 59 Subset9HKYGX.alpha 0.62780000 344070.0000
## 60 Subset10TRNEFG.kappa1 4.17550000 337400.0000
## 61 Subset10TRNEFG.kappa2 6.02560000 342470.0000
## 62 Subset10TRNEFG.alpha 0.50600000 338290.0000
## 63 Subset11JCG.alpha 0.38770000 282140.0000
## 64 Subset12TRNEFG.kappa1 3.08980000 324920.0000
## 65 Subset12TRNEFG.kappa2 7.49850000 321000.0000
## 66 Subset12TRNEFG.alpha 0.33910000 220060.0000
## 67 Subset1HKYGX.mu 1.17860000 183520.0000
## 68 Subset2TRNEFG.mu 0.86830000 155210.0000
## 69 Subset3TRNEFG.mu 0.28780000 112480.0000
## 70 Subset4GTRIGX.mu 0.71110000 105770.0000
## 71 Subset5K80IG.mu 0.81240000 89865.7064
## 72 Subset6SYMG.mu 1.65800000 86079.4311
## 73 Subset7K80G.mu 1.92930000 131010.0000
## 74 Subset8K80G.mu 1.05670000 92400.6821
## 75 Subset9HKYGX.mu 0.59180000 131650.0000
## 76 Subset10TRNEFG.mu 1.18620000 132450.0000
## 77 Subset11JCG.mu 0.20350000 110950.0000
## 78 Subset12TRNEFG.mu 2.54390000 79083.6503
## 79 ucld.mean 0.00073594 332.5618
## 80 ucld.stdev 0.98430000 2649.2935
## 81 meanRate 0.00068979 700.5540
## 82 coefficientOfVariation 1.27000000 226.7723
## 83 covariance 0.23290000 433.6862
## 84 Subset1HKYGX.treeLikelihood -1890.61200000 36770.2075
## 85 Subset2TRNEFG.treeLikelihood -2931.73400000 35236.5308
## 86 Subset3TRNEFG.treeLikelihood -2228.42960000 36860.1607
## 87 Subset4GTRIGX.treeLikelihood -4743.64810000 29583.0309
## 88 Subset5K80IG.treeLikelihood -5280.81190000 32435.6649
## 89 Subset6SYMG.treeLikelihood -5733.31740000 32350.1559
## 90 Subset7K80G.treeLikelihood -3537.00090000 26073.9083
## 91 Subset8K80G.treeLikelihood -8747.29310000 18666.6737
## 92 Subset9HKYGX.treeLikelihood -3646.88110000 24260.8424
## 93 Subset10TRNEFG.treeLikelihood -4255.05270000 39038.3017
## 94 Subset11JCG.treeLikelihood -949.70740000 9002.7411
## 95 Subset12TRNEFG.treeLikelihood -2630.61690000 25585.5111
## 96 branchRates 0.00000000 1000.0000
## 97 speciation -580.10770000 566.5915
Since all the ESS values exceeded 200, the sample from the posterior was summarized using TreeAnnotator:
java -Xmx20000m -cp /home/analysis/.linuxbrew/lib/beast.jar dr.app.tools.TreeAnnotator -burnin 50000000 -heights median concatchunks.trees chunks-BEAST.tre
Comparison with the MCMCTree-generated tree from the manuscript
In PAML, subsets of a concatenated alignment cannot be simply ignored as in BEAST but must be removed from the file, so that the sum of partition lengths equals the total number of nucleotides (PAML manual: p. 13). Moreover, the sites that make up a partition must be adjacent in the alignment. Therefore, the following information from the best_scheme file generated by PartitionFinder was used to generate a PAML-compatible alignment:
1 locus42, locus1
2 locus57, locus3, locus25, locus2
3 locus4
4 locus61, locus5, locus55, locus63, locus23, locus47
5 locus16, locus54, locus45, locus31, locus7, locus64, locus6
6 locus15, locus24, locus8, locus11, locus53, locus52, locus51, locus20, locus26
7 locus14, locus17, locus9, locus27, locus22, locus28
8 locus10, locus21, locus13
9 locus62, locus37, locus59, locus29, locus12, locus58, locus46, locus66, locus44, locus18
10 locus40, locus39, locus49, locus19, locus32, locus38
11 locus35, locus60, locus33, locus43, locus30
12 locus34
13 locus36, locus48, locus65
14 locus56, locus50, locus41
Using bash, replace whitespaces between each taxon name and the corresponding sequence with line breaks:
xargs -n 1 < concat.phy > concat2.phyManually remove the first line indicating the number of taxa and sites.
Now, use the structure of the new file (with names and sequences in alternating rows) to reorganize the chunks:
concat <- read.table("/Users/David/Downloads/concat2.phy", stringsAsFactors = FALSE)
# Random check, part 1: print "locus 42" (sites 2051--2100) of the first taxon:
substring(concat[2,], 2051, 2100)
for(i in seq(2, nrow(concat), by = 2)) {
chunks <- substring(concat[i,], seq(1, 3250, 50), seq(50, 3250, 50))
chunks[66] <- substring(concat[i,], 3251, 3297)
ch1 <- paste(chunks[42], chunks[1], sep = "")
ch2 <- paste(chunks[57], chunks[3], chunks[25], chunks[2], sep = "")
ch3 <- paste(chunks[61], chunks[5], chunks[55], chunks[63], chunks[23], chunks[47], sep = "")
ch4 <- paste(chunks[16], chunks[54], chunks[45], chunks[31], chunks[7], chunks[64], chunks[6], sep = "")
ch5 <- paste(chunks[15], chunks[24], chunks[8], chunks[11], chunks[53], chunks[52], chunks[51], chunks[20], chunks[26], sep = "")
ch6 <- paste(chunks[14], chunks[17], chunks[9], chunks[27], chunks[22], chunks[28], sep = "")
ch7 <- paste(chunks[10], chunks[21], chunks[13], sep = "")
ch8 <- paste(chunks[62], chunks[37], chunks[59], chunks[29], chunks[12], chunks[58], chunks[46], chunks[66], chunks[44], chunks[18], sep = "")
ch9 <- paste(chunks[40], chunks[39], chunks[49], chunks[19], chunks[32], chunks[38], sep = "")
ch10 <- paste(chunks[35], chunks[60], chunks[33], chunks[43], chunks[30], sep = "")
ch11 <- paste(chunks[36], chunks[48], chunks[65], sep = "")
ch12 <- paste(chunks[56], chunks[50], chunks[41], sep = "")
concat[i,] <- paste(ch1, ch2, ch3, ch4, ch5, ch6, ch7, ch8, ch9, ch10, ch11, ch12, sep = "")
}
# Make sure that the new sequence rows have the desired length:
for(i in seq(2, nrow(concat), by = 2)) {
print(nchar(concat[i,]))
}
# Random check, part 2: "locus 42" should now correspond to sites 1--50. Does it?
substring(concat[2,], 1, 50)
# Yes! Now print the new alignment into a table:
write.table(concat,
"/Users/David/Grive/Alfaro_Lab/Acanthomorpha/sate-gblocks-clean-min-90-taxa/Concat/pamlconcat.phy",
quote = FALSE,
row.names = FALSE,
col.names = FALSE)Add the following two lines to the beginning of the file:
118 3197 G
G 12 100 200 300 350 450 300 150 497 300 250 150 150Unlike BEAST, PAML cannot assign a separate substitution model to each partition, but it is capable of unlinking substitution model parameters across partitions (Warnock et al. 2014: ESM p. 2). Since moderate substitution model overparameterization usually does not pose a problem to Bayesian phylogenetic analyses (Ronquist & Deans 2010), each partition was assigned its own GTR+\(\Gamma\) (“REV”) model. Note that the unlinking of substitution models necessitates the use of empirical (nhomo = 0) rather than estimated base frequencies. Relative rate, equilibrium frequency, and alpha parameters were unlinked across partitions, but branch lengths were kept linked (options Mgene = 4 and Malpha = 1). To facilitate cross-platform comparisons, the root calibration was set to 120.5 Ma (same as the mean of the root prior used in BEAST) in baseml to calculate substitution rates.
The substitution rate estimation finished up in 5:46:48 and yielded the following values:
| Partition | Rate (subst. per 100 million years) |
|---|---|
| Gene 1 | 0.126608 |
| Gene 2 | 0.087695 |
| Gene 3 | 0.033200 |
| Gene 4 | 0.072678 |
| Gene 5 | 0.072362 |
| Gene 6 | 0.158487 |
| Gene 7 | 0.188372 |
| Gene 8 | 0.107932 |
| Gene 9 | 0.060550 |
| Gene 10 | 0.111402 |
| Gene 11 | 0.026772 |
| Gene 12 | 0.184536 |
For MCMCTree, the G option cannot be used, and the partitions must be given as multiple alignments one after another in a single file (http://groups.google.com/forum/#!topic/pamlsoftware/cC7mOgZnNiY). Such a file was compiled as follows:
Run the following code:
concat <- read.table("/Users/David/Downloads/concat2.phy", stringsAsFactors = FALSE)
alignments <- matrix(nrow = 12*(nrow(concat) + 2), ncol = 1)
for(i in 1:(nrow(concat)/2)) {
for(j in 0:11) {
alignments[(2*i-1) + j*(nrow(concat) + 2), ] <- concat[(2*i-1),]
}
chunks <- substring(concat[2*i,], seq(1, 3250, 50), seq(50, 3250, 50))
chunks[66] <- substring(concat[2*i,], 3251, 3297)
alignments[2*i,] <- paste(chunks[42], chunks[1], sep = "")
alignments[2*i + 1*(nrow(concat) + 2),] <- paste(chunks[57], chunks[3], chunks[25], chunks[2], sep = "")
alignments[2*i + 2*(nrow(concat) + 2),] <- paste(chunks[61], chunks[5], chunks[55], chunks[63], chunks[23], chunks[47], sep = "")
alignments[2*i + 3*(nrow(concat) + 2),] <- paste(chunks[16], chunks[54], chunks[45], chunks[31], chunks[7], chunks[64], chunks[6], sep = "")
alignments[2*i + 4*(nrow(concat) + 2),] <- paste(chunks[15], chunks[24], chunks[8], chunks[11], chunks[53], chunks[52], chunks[51], chunks[20], chunks[26], sep = "")
alignments[2*i + 5*(nrow(concat) + 2),] <- paste(chunks[14], chunks[17], chunks[9], chunks[27], chunks[22], chunks[28], sep = "")
alignments[2*i + 6*(nrow(concat) + 2),] <- paste(chunks[10], chunks[21], chunks[13], sep = "")
alignments[2*i + 7*(nrow(concat) + 2),] <- paste(chunks[62], chunks[37], chunks[59], chunks[29], chunks[12], chunks[58], chunks[46], chunks[66], chunks[44], chunks[18], sep = "")
alignments[2*i + 8*(nrow(concat) + 2),] <- paste(chunks[40], chunks[39], chunks[49], chunks[19], chunks[32], chunks[38], sep = "")
alignments[2*i + 9*(nrow(concat) + 2),] <- paste(chunks[35], chunks[60], chunks[33], chunks[43], chunks[30], sep = "")
alignments[2*i + 10*(nrow(concat) + 2),] <- paste(chunks[36], chunks[48], chunks[65], sep = "")
alignments[2*i + 11*(nrow(concat) + 2),] <- paste(chunks[56], chunks[50], chunks[41], sep = "")
}
write.table(alignments, "/Users/David/Downloads/pamlconcatpartitioned.phy", quote = FALSE, row.names = FALSE, col.names = FALSE, na = "")Manually add the information about the number of taxa and sites in each partition.
To obtain a single estimate that could be used for the gamma-Dirichlet hyperprior on rates (rgene_gamma), a weighted average of these values was computed, with each rate weighted by the length of the corresponding partition:
## [1] 0.09550072
The shape parameter of the gamma-Dirichlet distribution (\(\alpha\)) was set to 2 and the rate parameter (\(\beta\)) was chosen so that the mean (calculated as \(\frac{\alpha}{\beta}\)) was equal to the rate above (expressed as the number of substitutions per 10 million years):
## [1] 209.4225
The hyperprior on the mean of the rate distribution is distributed as follows:
The mean of the gamma hyperprior on the variance of the log rate was set to 0.1 by setting \(\alpha\) equal to 1 and \(\beta\) equal to 10. This corresponds closely to the mean of the ucld.stdev hyperprior in BEAST (0.3 – note that while BEAST places the prior on the standard deviation of the rate distribution, MCMCTree assigns the prior to the variance, or the square of the standard deviation).
The lognormal distribution of rates is plotted below, with the mean and variance (in log-space) set equal to the means of the respective hyperpriors:
Finally, since there are multiple loci, the Dirichlet concentration parameter \(\alpha_D\) was specified and set to the default value of 1, which is described as producing “a reasonable partitioning” in the MCMCTree manual.
The full configuration file is shown below:
seed = -1
seqfile = pamlconcatpartitioned.phy
treefile = 12_cali_no_outgroups_corrected.tre
outfile = chunks.txt
ndata = 12 * Number of partitions
seqtype = 0 * Data type: nucleotides
usedata = 3 * Store the Hessian matrix for approximate likelihood computation in out.BV
clock = 2 * Uncorrelated lognormal relaxed clock
RootAge = 'B(9.8, 14.3, 1e-300, 0.05)' * P of less than 98 Ma = 10^(-300) and P of more than 143 Ma = 0.05
model = 7 * GTR
alpha = 0.1 * Following Alfaro et al.
ncatG = 8 * Following Alfaro et al.
cleandata = 0 * Do not remove sites with ambiguity
BDparas = 0.1 0.1 00.1 * Birth, death, sampling: following Alfaro et al.
kappa_gamma = 6 2 * No effect since usedata = 3
alpha_gamma = 1 1 * No effect since usedata = 3
rgene_gamma = 2 209.42 1 * Gamma-Dirichlet prior on mean rate: estimated using baseml under strict clock
sigma2_gamma = 1 10 1 * Gamma-Dirichlet prior on log rate variance
finetune = 1: .1 .1 .1 .1 .1 .1 * Auto finetune: times, musigma2, rates, mixing, paras, FossilErr
print = 2 * Print branch rates into an output file
burnin = 500000 * Following Alfaro et al.
sampfreq = 500 * Following Alfaro et al.
nsample = 15000 * Following Alfaro et al.
The initial analyses (run under the usedata = 3 option to calculate the Hessian matrices for the branch lengths) produced the following warning messages:
xmax = 0.0000e+00 close to zero at 226!
xmax = 0.0000e+00 close to zero at 225!
xmax = 0.0000e+00 close to zero at 223!
xmax = 0.0000e+00 close to zero at 224!
However, these did not cause baseml to crash, and the Hessian matrices were successfully written into out.BV files. Therefore, the usedata variable was set to 2, the out.BV files were moved to in.BV, and four separate MCMC chains with a length of 75 million generations (as specified in the configuration file above) were started.
Ronquist F, Deans AR 2010 Bayesian phylogenetics and its influence on insect systematics. Annu Rev Entomol 55: 189–206
Warnock RCM, Parham JF, Joyce WG, Lyson TR, Donoghue PCJ 2014 Calibration uncertainty in molecular dating analyses: there is no substitute for the prior evaluation of time priors. Proc R Soc B 282(1798): 20141013